DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This office action is in response to the most recent filings filed by applicant on 01/28/26.
Claims 1, 11 and 20 are amended
Claims 4-6, 8, and 14-17 are cancelled
No claims are added
Claims 1-3, 7, 9-13, and 18-20 are pending
Note:
Applicants have amended the claims to add more structure, which is great. The only thing is the amended claims are not tieing additional elements like “using at least one first trained machine learning model at the supplier computer system”, “by the at least one first trained machine learning model”, “using at least one second trained machine learning model” to the judicial exception. The additional elements are recited at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Given that the specification recites terms like “machine learning” at such a high level, for instance, the only place in the originally submitted specification the terms machine learning is recited in [0031] In various implementations, the models 111 can include or utilize a machine learning model or other predictive tool. In general, any suitable machine learning or predictive modeling technique can be used, such as, for example: a gradient boosted random forest, a regression, a neural network, a decision tree, a support vector machine, a Bayesian network, or other type of technique. The models 111 can be trained using a set of training data. The training data can be or include, for example, historical data related to accounts, including sales data, inventory data, demographics data, geographical location, and the like. Such data can include information related to the model input parameters and one or more target features that the model is trying to predict (e.g., an objective or goal for an account). In general, the models 111 can be trained to recognize how to optimize, maximize, or minimize one or more target features associated with an account (e.g., sales or product inventory). Once trained, the models 111 can receive the input parameters as input and provide one or more goals or objectives as output.
Since, the specification is lacking details on how the steps are carried out, it maybe harder to tie the additional elements into the claim, but if applicants are able to do that, it will help make the scope of the claim clearer. For instance:
- The amended claim limitation recites: “determining, using at least one first trained machine learning model at the supplier computer system, a similarity between a first retail account of the plurality of retail accounts in a first geographical location and a second retail account of the plurality of retail accounts in a second geographical location different from the first geographical location, wherein the similarity comprises a quantitative measure generated by the at least one first trained machine learning model based on account attributes comprising sales history, demographic data, socio-economic information, and geographic information;” Here, the trained machine learning model is being used to find similarity between a first and a second retail account in a geographical location. There is no detail to show how this is being done? What are the metrics, variables, calculations that are helping you make this determination? Simply reciting “wherein the similarity comprises a quantitative measure generated by the at least one first trained machine learning model based on account attributes comprising sales history, demographic data, socio-economic information, and geographic information” does not tell you any detail on how this is carried out? A quantitative measure is super broad. Comparing sales history, demographic data, etc. for two retails stores in a similar geographic location can be done in the human mind. How is the machine learning model doing this differently? How is this improving the technology or technological environment?
Additionally, “preventing, by the security gate, the status update from being…. thereby improving data security; … providing, …. the supply value indicative of a product for the second retail account; preventing, …. authorizes the supply value update for sharing in accordance with the distributor-controlled approval rules, thereby improving security of the supply value update; and …. thereby improving computer processing requirements and improving bandwidth utilization, by processing and transmitting only security gate-approved data.”
These limitations are simply reciting approving an update based on certain rules. The additional detail in these limitations does not add anything more to the technology or technological space. Approving an update based on certain rules benefits any general-purpose computer by providing “improving security, improving computer processing requirements and improving bandwidth utilization”. It is unclear how the machine learning model is helping make this process better beyond what is obvious to one of ordinary skill in the art for a general-purpose computer.
In light of these notes, the amended claims, do not overcome previously presented rejections under 101 and 103. As is discussed below. This note is intended as a conversation starter to help applicants understand the examiner’s perspective. Applicants are welcome to call the examiner to discuss this further.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 7, 9-13, and 18-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-3, 7 and 9-10 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 11-13, and 18-19 is/are directed to a system which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim 20 is/are directed to an article, comprising: a non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more computer processors, cause the computer processors to perform operations comprising: which is a statutory category.
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
With respect to the Step 2A, Prong One, the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Claim 1 is directed to an abstract idea, as evidenced by claim limitations “wherein managed by a supplier of a plurality of products, is managed by a distributor of the plurality of products, and the retail computer system is associated with a plurality of retail accounts; determining, a similarity between a first retail account of the plurality of retail accounts in a first geographical location and a second retail account of the plurality of retail accounts in a second geographical location different from the first geographical location, wherein the similarity comprises a quantitative measure generated based on account attributes comprising sales history, demographic data, socio-economic information, and geographic information; determining, a sales objective for the second retail account based on the similarity; synchronizing, by receiving updates of the sales objective in data payloads and storing the sales objective; receiving, from the API a status update generated via an input related to the sales objective; transmitting the status update, and preventing, the status update at least until the distributor computer system authorizes sharing of the status update in accordance with distributor-controlled approval rules so that only information agreed to be shared back to the supplier is shared, thereby improving data security; after the status update is no longer prevented from being received: providing, a supply value update for updating a supply value stored in a data lake associated, the supply value indicative of a product for the second retail account; preventing, the supply value from being modified at least until the distributor computer system authorizes the supply value update for sharing in accordance with the distributor-controlled approval rules, thereby improving security of the supply value update; and after the security gate is no longer preventing the supply value from being modified, updating, by the supplier computer system, a record in the data lake using the supply value update.”
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to account management and product distribution (see Specification [0004]). In the specification, [0004] recites: [0004] In general, suppliers understand brand and product strategy and can struggle to activate that in the marketplace effectively without a strong connection to the sales call or sales representative at the retail account. From the supplier’s perspective, however, a key challenge of the three-tier distribution system is engaging the right account with the night product at the right time. Therefore, there is a need for improved systems and methods for account management and product distribution. Here, the applicants are trying to solve a business problem, not a technological problem.
Managing accounts and product distributions for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
In addition, claim limitations such as, “receiving, at the supply management platform and from the API a status update generated via an input in the client device related to the sales objective; transmitting the status update from the supply management platform over a streaming service to a security gate implemented by the distributor computer system, and preventing, by the security gate, the status update from being received by the supplier computer system at least until the distributor computer system authorizes sharing of the status update in accordance with distributor-controlled approval rules so that only information agreed to be shared back to the supplier is shared, thereby improving data security;” are abstract because the claim limitations are simply stating that a status update is received, an adjustment of a supply value is facilitated. Confirming supply value update and updating the stored supply value. This is simply gathering status information and then updating supply values. The supply value updates are not serving any purpose, there is no actual use of this supply value and stored supply value update. As the claim is written now, when read in its broadest reasonable interpretation, the claim is simply updating supply and stored supply values and that are sitting in some database and not being utilized for anything else.
Independent Claims 11 and 20 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim only recites “A computer-implemented method comprising: providing a supply management platform in communication with a supplier computer system, a distributor computer system, and a retail computer system, the supplier computer system is, the distributor computer system, wherein the supply management platform includes a central data repository and data exchange that is accessible by the supplier computer system, the distributor computer system, and the retail computer system; using at least one first trained machine learning model at the supplier computer system, by the at least one first trained machine learning model, at the supplier computer system, using at least one second trained machine learning model in the supplier computer system, via an application programming interface (API), a client device with the supply management platform, in local memory of the client device for offline execution, wherein the retail computer system comprises the client device, wherein the client device is a device of a sales representative for the second retail account; at the supply management platform and, in the client device, from the supply management platform over a streaming service to a security gate implemented by the distributor computer system, by the security gate, from being received by the supplier computer system, by the supplier computer system, to the security gate, with the supplier computer system, by the security gate, and providing a direct data exchange between the supplier computer system, the distributor computer system, and the retail computer system by routing security gate-approved updates through the streaming service, thereby improving computer processing requirements and improving bandwidth utilization, by processing and transmitting only security gate-approved data”, such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
As a result, claims 1, 11 and 20 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception.
Similarly dependent claims 2-3, 7, 9-10, 12-13, and 18-19 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “wherein the supplier, the distributor, and the plurality of retail accounts comprise a three-tier distribution system, and wherein the plurality of products comprise alcoholic beverages.” Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above. Similarly, claims 3-5, recite: Claim 3: wherein the at least one sales objective relates to adjusting at least one of a volume or a type of product being sold at the at least one of the retail accounts. Claim 4: wherein the at least one predictive model is configured to determine similarities among the plurality of retail accounts and derive the at least one sales objective based on the similarities. Claim 5: wherein the similarities relate to at least one of demographics or sales histories for the plurality of retail accounts. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 6-7 and 9-10: recite, claim 6: “wherein the at least one predictive model comprises a nearest neighbors model.” Claim 7: “wherein providing the at least one sales objective to the retail computer system comprises providing an incentive to the sales representative for completion of the at least one sales objective.” Claim 9: “wherein the at least one sales objective was modified using the distributor computer system.” Claim 10: “wherein the supply management platform includes at least one of a digital asset module, a retail account module, a sale rep and hierarchy module, a product information module, a goals module, or a routes and territory module.” In the above claims, claim 6: “a nearest neighbors model”, claim 7: “at least one sales objective to the retail computer system”, claim 9: “using the distributor computer system”, claim 10: “supply management platform … a digital asset module, a retail account module, a sale rep and hierarchy module, a product information module, a goals module, or a routes and territory module” are additional elements, but are still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-3, 7, 9-10, 12-13, and 18-19 are also directed to the abstract idea identified above.
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A computer-implemented method comprising: providing a supply management platform in communication with a supplier computer system, a distributor computer system, and a retail computer system, the supplier computer system is, the distributor computer system, wherein the supply management platform includes a central data repository and data exchange that is accessible by the supplier computer system, the distributor computer system, and the retail computer system; using at least one first trained machine learning model at the supplier computer system, by the at least one first trained machine learning model, at the supplier computer system, using at least one second trained machine learning model in the supplier computer system, via an application programming interface (API), a client device with the supply management platform, in local memory of the client device for offline execution, wherein the retail computer system comprises the client device, wherein the client device is a device of a sales representative for the second retail account; at the supply management platform and, in the client device, from the supply management platform over a streaming service to a security gate implemented by the distributor computer system, by the security gate, from being received by the supplier computer system, by the supplier computer system, to the security gate, with the supplier computer system, by the security gate, and providing a direct data exchange between the supplier computer system, the distributor computer system, and the retail computer system by routing security gate-approved updates through the streaming service, thereby improving computer processing requirements and improving bandwidth utilization, by processing and transmitting only security gate-approved data” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0018], [0035]-[0040], [0047]-[0054]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 11 and 20 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-3, 7, 9-10, 12-13, and 18-19 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 11 and 20. As a result, Examiner asserts that dependent claims, such as dependent claims 2-3, 7, 9-10, 12-13, and 18-19 are also directed to the abstract idea identified above.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103 - withdrawn
In the most recent filings, applicants have amended independent claims 1, 11 and 20. Please see the Remarks dated 09/30/2025, especially on pages 17-19 from applicants why the 103 rejection is overcome are persuasive.
In light of applicants’ arguments and the amendments filed by applicants to previously presented claims in light of the originally filed disclosure the previously made rejection under 35 U.S.C. 103 has been withdrawn for following reasons:
None of the references cited - (US 2004/0193482) Hoffman et al., and further in view of (US 2003/0097317) Burk et al. and (US 20140201001) Rellas et al. show the claim limitations discussed below in light of the specification.
In the amended claims, even given the breadth of the claim limitations (see note above) the underlined limitations are not found in prior art and as such prior art is withdrawn:
“A computer-implemented method comprising:
providing a supply management platform in communication with a supplier computer system, a distributor computer system, and a retail computer system,
wherein the supplier computer system is managed by a supplier of a plurality of products, the distributor computer system is managed by a distributor of the plurality of products, and the retail computer system is associated with a plurality of retail accounts;
wherein the supply management platform includes a central data repository and data exchange that is accessible by the supplier computer system, the distributor computer system, and the retail computer system;
determining, using at least one first trained machine learning model at the supplier computer system, a similarity between a first retail account of the plurality of retail accounts in a first geographical location and a second retail account of the plurality of retail accounts in a second geographical location different from the first geographical location, wherein the similarity comprises a quantitative measure generated by the at least one first trained machine learning model based on account attributes comprising sales history, demographic data, socio-economic information, and geographic information;
determining, at the supplier computer system, a sales objective for the second retail account based on the similarity using at least one second trained machine learning model in the supplier computer system;
synchronizing, via an application programming interface (API), a client device with the supply management platform by receiving updates of the sales objective in data payloads and storing the sales objective in local memory of the client device for offline execution;
wherein the retail computer system comprises the client device, wherein the client device is a device of a sales representative for the second retail account;
receiving, at the supply management platform and from the API a status update generated via an input in the client device related to the sales objective;
transmitting the status update from the supply management platform over a streaming service to a security gate implemented by the distributor computer system, and
preventing, by the security gate, the status update from being received by the supplier computer system at least until the distributor computer system authorizes sharing of the status update in accordance with distributor-controlled approval rules so that only information agreed to be shared back to the supplier is shared, thereby improving data security;
after the status update is no longer prevented from being received by the supplier computer system:
providing, to the security gate, a supply value update for updating a supply value stored in a data lake associated with the supplier computer system, the supply value indicative of a product for the second retail account;
preventing, by the security gate, the supply value from being modified at least until the distributor computer system authorizes the supply value update for sharing in accordance with the distributor-controlled approval rules, thereby improving security of the supply value update; and
after the security gate is no longer preventing the supply value from being modified, updating, by the supplier computer system, a record in the data lake using the supply value update;
and providing a direct data exchange between the supplier computer system, the distributor computer system, and the retail computer system by routing security gate-approved updates through the streaming service, thereby improving computer processing requirements and improving bandwidth utilization, by processing and transmitting only security gate-approved data.”
Even though, Reference Hoffman shows in Hoffman: [0006] A system, method and computer program product are disclosed for advertising in a network-based supply chain management framework in which data is received utilizing a network from a plurality of stores of a supply chain. A user is allowed to access the data utilizing a network-based interface. The user accessing the network-based interface is identified and advertising is presented to the user in accordance with the identification. Hoffman: [0257] Supply chain participants may also include brand owners, point of sale outlets, point of sale outlet owners, a cooperative or consortium of point of sale outlet owners, distributors, or suppliers. Suppliers may supply one or more of finished goods, partially finished goods or raw materials. [0258] The supply chain management system of the present invention includes six system components which may be integrated independently, on a parallel path, but ultimately are able to electronically interface with each other. Typically, a supply chain may include retailers, distributors and suppliers or equivalents thereof. Fig. 1A, Data Warehouse 112 reads on “a central data repository and data exchange”, Supplier 108, Distributor 106, Point of Sale Outlet 104. [0259] The supply chain management system according to one aspect of the present invention, increases the Quality Of Service (QOS) to supply chain participants, lowers costs and adds new value to supply chain participants with its "predictive" nature based on statistically driven models, discussed below. [0304] Sales forecasting and inventory management are components in an embodiment of the Supply Chain management system. A theme of this model is transparent communication of current (i.e. virtually real-time) and expected sales to some or all supply chain participants in a statistically meaningful distribution everyday for all inventory level products. In other words, predictive supply chain behavior can be determined and analyzed. Of course, the counterbalance here is the commitment to maintain the confidentiality of the particular data source/franchisee. [0307] One aspect of the present invention provides an analytic model which enables a large and extended ecosystem, comprised of many similar but otherwise independent operating units, to quickly and inexpensively share near-real time data, with a trusted 3rd party, from a selected (and non-disclosed) sources, in a highly granular format, and then have extracted meaningful projections of future behavior for all of the other independent operating units so as to effect their purchase decisions. The combination of (a) confidential and very specific data, (b) accumulated quickly and cheaply, (c) shared to similar operating units, (d) leading to predictive supply chain decisions for the benefit of manufactures, suppliers, distributors and operators is a major benefit provided by the present invention. Hoffman: [0273] Data Warehouse 112 is a central collection point that electronically collects and warehouses timely, critical Supply Chain information for all Supply Chain participants. This includes distributor and supplier performance measures, representations of daily outlet item sales with translations to specified product requirements, and inventory levels, sales history and forecasts at various points in the Supply Chain, thereby providing a basis for collaborative planning and forecasting. The data stored in the Warehouse is then available for quick, secure access. [0314] FIG. 13 is a flowchart of a process 1330 for planning promotions in which historical data is collected utilizing a network from a plurality of stores of a supply chain in operation 1332. This historical data relates to at least the sale of goods by the stores and can be further categorized based on seasonality, past marketing and/or advertising support, etc. A promotion is then planned based on the historical data in operation 1334 and this planning is subsequently communicated to the stores utilizing the network in operation 1336. [0304] Sales forecasting and inventory management are components in an embodiment of the Supply Chain management system. A theme of this model is transparent communication of current (i.e. virtually real-time) and expected sales to some or all supply chain participants in a statistically meaningful distribution every day for all inventory level products. In other words, predictive supply chain behavior can be determined and analyzed. Of course, the counterbalance here is the commitment to maintain the confidentiality of the particular data source/franchisee. [0307] One aspect of the present invention provides an analytic model which enables a large and extended ecosystem, comprised of many similar but otherwise independent operating units, to quickly and inexpensively share near-real time data, with a trusted 3rd party, from a selected (and non-disclosed) sources, in a highly granular format, and then have extracted meaningful projections of future behavior for all of the other independent operating units so as to effect their purchase decisions. The combination of (a) confidential and very specific data, (b) accumulated quickly and cheaply, (c) shared to similar operating units, (d) leading to predictive supply chain decisions for the benefit of manufacturers, suppliers, distributors and operators is a major benefit provided by the present invention. [0285]-[0287]: the performance may be tracked by comparing the delivery dates with a plurality of target dates. As another aspect, the performance may be tracked by comparing the delivery dates with delivery dates associated with other distributors. In another aspect, the performance may be displayed to the stores utilizing a network-based interface. In a further aspect, the data relating to the distribution of goods may be received from the stores. [0289] In one aspect, the data includes delivery dates associated with the goods. In such an aspect, the performance may be tracked by comparing the delivery dates with a plurality of target dates. As another aspect, the performance may be tracked by comparing the delivery dates with delivery dates associated with other distributors. In another aspect, the performance is displayed to the stores utilizing a network-based interface. In a further aspect, the data includes inventory levels associated with the goods. In such an aspect, the performance may be tracked by comparing the inventory levels with a plurality of target inventory levels. As another aspect, the performance may be tracked by comparing the inventory levels with inventory levels associated with other suppliers. [1653] The new technological infrastructure and its associated electronic reporting and feedback systems equips retailer management with accurate, timely, and previously unavailable information from the Supply Chain on sales, marketing and other performance indicators allow Supply Chain management to fully engage in managing supply and distribution processes and channels toward identified and agreed strategic objectives provide franchisees and retailers with the Supply Chain information they need to operate efficiently and make effective management decisions minimally impacts the resources of Supply Chain management. [0367] FIG. 29 is a flowchart of a process 2930 for generating supply chain statistics. Data is received utilizing a network from a plurality of stores, distributors and suppliers of a supply chain in operation 2932. Preferably, the data is received from less than all of the stores, distributors and suppliers to generate closely-controlled representative statistics. The data is sampled in operation 2934 and supply chain statistics are generated based on the sampling in operation 2936. The generated supply chain statistics are utilized for demand forecasting, advance planning, and/or volume tracking in the supply chain in operation 2938. [0368] In an aspect, the sampling may be representative of a predetermined percentage of the stores, distributors, and suppliers. In another aspect, the statistics may represent sales of the stores. In a further aspect, the statistics may represent goods ordered by the stores. In an additional aspect, the statistics may represent a timeliness of delivery of the ordered goods by the distributors. In one aspect, the statistics may represent an inventory of the suppliers. However, the reference does not show the above claim limitations.
Reference Rella shows “a status update via an input in the client device” in [0130]: In some examples, all calls are sent via HTTP post to the server which processes the requests and sends back JSON objects with the appropriate data. [0207]: A reference to the transaction ID on the third-party payment processor gateway may also be kept, but these references may not be visible to the driver application. In some examples, all calls are sent via HTTP post to the server which processes the requests and sends back JSON objects with the appropriate data. The client (driver application) parses this data and displays the orders in a scrolling table-based view. Reference Rella shows “a security gate” in [0107]: A specific example could be a partnership between a host and an identification verification company to develop an identification characterization, verification or authentication application that can be used by the government (e.g., at security check points), in-store sellers (e.g., for identification checks in gun stores or liquor stores), and bouncers (e.g., at the entrance to night clubs or casinos) and in many other industries and situations. However, the reference does not show the above claim limitations.
Reference Burk shows an adjustment for the status update at least in [0294]: Supply Chain management is able to provide online local promotion information to distribution centers, suppliers, Field Marketing, ADIs and Local Distribution Committees. This improves the speed to market for promotions and new products, as well as provides the ability to make ongoing program adjustments. [0551]: providing a supplier interface. Utilizing a network, data is received from a plurality of stores of a supply chain in operation 5132. This data relates to an amount of goods sold by the stores. The data is aggregated in a database in operation 5134. Subsequently, a request is received from a supplier which includes a plurality of supplier parameters in operation 5136. Information from the database relevant to the supplier parameters is extracted in response to the request in operation 5138 and the information from the database is transmitted to the supplier utilizing the network in operation 5140. Also, a supply of raw materials from which the goods are produced is adjusted based on the information in operation 5142. Note also that the amount/rate of finishing goods and/or supplies can be adjusted based on the information. [0553]: providing a distributor interface. Data is received from a plurality of stores of a supply chain utilizing a network in operation 5232. This data relates to an amount of goods sold by the stores and is aggregated in a database in operation 5234. Upon receiving a request which includes a plurality of distributor parameters from a distributor in operation 5236, information is extracted in operation 5238 from the database relevant to the distributor parameters in response to the request. The information is then transmitted from the database to the distributor utilizing the network in operation 5240 and an amount of raw materials purchased in correlation to the production of the goods is adjusted based on the information in operation 5242. However, the reference does not show the above claim limitations.
*Additionally, the prior art made of record and not relied upon is considered pertinent to applicant's disclosure; however, the reference does not show the above claim limitations:
NPL Reference:
Reference J. -f. Zhao, "A new kind of retailer supply chain system model," 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management, Changchun, China, 2011, pp. 11-14, doi: 10.1109/ICIEEM.2011.6035094. This reference discloses in this paper, a retail supply chain system model, FROM-SCM was provided according the actual situation of China's retail enterprises. The commodity demand forecasting, pricing and discounts, and order policy, and other key issues to improve the model and its realization in FROM-SCM based on TEI@I methodology were discussed. A new kind of retail supply chain management model that can optimize integration of external resources, greatly improve the efficiency of the supply chain was certificated by empirical research using FROM-SCM model. However, the reference does not show the claim limitations above.
Foreign Reference:
Reference (CN 105894299 A) Zhou et al. Shop Management System for Electronic Commerce. This reference discloses a shop management system of electronic commerce can be more scientific management of orders, shipping, logistics, each shop selling under the same brand and then customer management problem. It comprises a shop setting module, an order processing module, a dispatching processing module, after-sale service module, the client management module and a central processor, network shop setting module, an order processing module, a dispatching processing module, after-sale service module; client management module are connected with the central processor; the shop setting module comprises a basic information unit, an authentication information unit and a website link unit, order processing module is used for inquiring the shop order and checking the order validity, destination processing module used to define the logistic distribution department and the effective order delivery processing; after-sale service module for tracking order logistics information, registration problem and processing flow after sale of the merchandise, the client management module is used for collecting the online store customer information, analyzing customer consumption habit and area. However, the reference does not show the claim limitations above.
None of the prior art of record, taken individually or in combination, teach, interalia, the claimed invention as detailed in independent claims 1, 11 and 20, wherein the novelty of the claimed invention is in the combination of limitations and not in any single limitation.
Response to Arguments
Applicants’ arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Please see the detailed NOTE above at the beginning of the office action. Applicants arguments related to 101 have been fully considered; however applicants arguments are unpersuasive.
The additional elements are recited at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
J. -f. Zhao, "A new kind of retailer supply chain system model," 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management, Changchun, China, 2011, pp. 11-14, doi: 10.1109/ICIEEM.2011.6035094.
Foreign Reference:
(CN 105894299 A) Zhou et al. Shop Management System for Electronic Commerce.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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